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On the Complexity of Generalized Chromatic Polynomials

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On the Complexity of Generalized Chromatic Polynomials View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Birkbeck Institutional Research Online On the Complexity of Generalized Chromatic Polynomials A. Goodalla,2, M. Hermannb,1, T. Kotekc,3, J.A. Makowskyd,1,∗, S.D. Noblee,2 aIUUK, MFF, Charles University, Prague, Czech Republic bLIX (CNRS, UMR 7161), Ecole´ Polytechnique, 91128 Palaiseau, France cTechnische Universitat¨ Wien, Institut fur¨ Informationssysteme, 1040 Wien, Austria dDepartment of Computer Science, Technion–IIT, 32000 Haifa, Israel eDepartment of Economics, Mathematics and Statistics, Birkbeck, University of London, London, United Kingdom Abstract J. Makowsky and B. Zilber (2004) showed that many variations of graph colorings, called CP-colorings in the sequel, give rise to graph polynomials. This is true in par- ticular for harmonious colorings, convex colorings, mcct-colorings, and rainbow color- ings, and many more. N. Linial (1986) showed that the chromatic polynomial χ(G; X) is #P-hard to evaluate for all but three values X = 0; 1; 2, where evaluation is in P. This dichotomy includes evaluation at real or complex values, and has the further prop- erty that the set of points for which evaluation is in P is finite. We investigate how the complexity of evaluating univariate graph polynomials that arise from CP-colorings varies for different evaluation points. We show that for some CP-colorings (harmo- nious, convex) the complexity of evaluation follows a similar pattern to the chromatic polynomial. However, in other cases (proper edge colorings, mcct-colorings, H-free colorings) we could only obtain a dichotomy for evaluations at non-negative integer points. We also discuss some CP-colorings where we only have very partial results. Keywords: Graph polynomials, Counting Complexity, Chromatic Polynomial, MSC classes: 05C15, 05C31, 05C85, 68Q17, 68W05 ∗Corresponding author Email addresses: [email protected] (A. Goodall), [email protected] (M. Hermann), [email protected] (T. Kotek), [email protected] (J.A. Makowsky), [email protected] (S.D. Noble) URL: http://kam.mff.cuni.cz/˜andrew (A. Goodall), http://www.lix.polytechnique.fr/Labo/Miki.Hermann (M. Hermann), http://forsyte.at/˜kotek/ (T. Kotek), http://www.cs.technion.ac.il/˜janos (J.A. Makowsky), http://www.bbk.ac.uk/ems/faculty/steven-noble (S.D. Noble) 1Work done in part while the authors were visiting the Simons Institute for the Theory of Computing in Spring 2016. 2Supported by the Heilbronn Institute for Mathematical Research, Bristol, UK. 3Supported by the Austrian National Research Network S11403- N23 (RiSE) of the Austrian Science Fund (FWF). Preprint submitted to Elsevier April 3, 2017 Contents 1 Introduction 3 1.1 The complexity spectrum . .4 1.2 Easy computation of the polynomial versus its easy evaluation . .5 1.3 Linial’s Trick . .5 1.4 The Difficult Point Dichotomy . .7 2 One, two, many chromatic polynomials 8 2.1 Many chromatic polynomials . .8 2.2 P-colorings and variations . 10 3 Detailed case study: Dichotomy theorems 11 3.1 Harmonious colorings . 11 3.2 Convex colorings . 13 3.3 DU(H)-colorings . 14 4 Counting convex colorings is #P-complete: the proof 17 4.1 Cuts, crossing sets, and cocircuits . 17 4.2 Reductions . 18 5 Detailed case study: Discrete spectra 21 5.1 Proper edge colorings . 21 5.2 mcct-colorings . 22 5.3 H-free-colorings . 25 6 More graph polynomials 26 6.1 Graph polynomials with incomplete complexity spectrum . 26 6.2 NP-hardness . 27 7 Conclusions and open problems 28 2 1. Introduction By a classical result of R. Ladner, and its generalization by K. Ambos-Spies, [Lad75, AS87], there are infinitely many degrees (via polynomial time reducibility) between P and NP, and between P and #P, provided P 6= NP. In contrast to this, the com- plexity of evaluating partition functions or counting graph homomorphisms satisfies a dichotomy theorem: either evaluation is in P or it is #P-complete, [DG00, BG05, CCL13]. For the definition of the complexity class #P, see [GJ79] or [Pap94]. In accordance with the literature in graph theory a finite graph G = (V (G);E(G)) with n(G) = jV (G)j and e(G) = jE(G)j has order n(G) and size e(G). Otherwise, the size of a finite set is its cardinality. In this paper we study the complexity of the evaluation of generalized univari- ate chromatic polynomials, as introduced in [MZ06] and further studied in [KMZ08, KMZ11]. They will be called in the sequel CP-colorings (for Counting Polynomials). Among these we find: Examples 1.1. (i) Trivial (unrestricted) vertex colorings using at most k colors are just functions V (G) ! [k]. We denote by χtrivial(G; k) the number of trivial jV (G)j colorings of G, hence χtrivial(G; k) = k 2 Z[k]. (ii) Proper vertex colorings using at most k colors, where two neighboring vertices receive different colors, are counted by χ(G; k), the classical chromatic polyno- mial . (iii) Proper edge colorings using at most k colors, where two edges with a common vertex receive different colors, are counted by χedge(G; k), the edge chromatic polynomial. We note that they are exactly the proper vertex colorings of the line graph L(G) of G. (iv) Convex colorings using at most k colors are vertex colorings, which are not nec- essarily proper, but where each color class induces a connected subgraph. They are counted by χconvex(G; k). Convex colorings are first introduced in [MS07]. (v) Harmonious colorings using at most k colors are proper vertex colorings such that no two edges have end-vertices receiving the same pair of colors. They were introduced in [HK83, EM95, Edw97]. We denote the number of harmo- nious colorings using at most k colors by χharm(G; k). The graph parameter χharm(G; X) is a polynomial in k by [MZ06, KMZ11] which was further studied more recently in [DBG17]. (vi) For a fixed connected graph H, DU(H)-colorings are vertex colorings, where each color class induces a disjoint collection of copies of H. The graph parame- ter counting the number of DU(H)-colorings with at most k colors is a polyno- mial in k, and the corresponding graph polynomial is denoted by χDU(H)(G; k). + (vii) For a fixed t 2 N , an mcct-coloring using at most k colors is a vertex col- oring, where the connected components of the subgraphs induced by each color class have at most t vertices. They were previously studied in [ADOV03] and [LMST07]. The graph parameter χmcct (G; k) counting the number of mcct- colorings with at most k colors is also a polynomial in k but not in t. (viii) For a fixed graph H, an H-free coloring using at most k colors is a vertex color- ing in which every color class induces an H-free graph. For H = K2 these are 3 the proper vertex colorings. The graph parameter χH−free(G; k) counting the number of H-free colorings with at most k colors is also a polynomial in k. More examples are presented in Section 2, where we also discuss a general theorem which allows us to find infinitely many generalized chromatic polynomials, and in Section 6. 1.1. The complexity spectrum Let F be a fixed field that contains Q, the rational numbers, and in which the arithmetic operations are polynomial time computable. For our discussion we use the unit-cost4 model for the field computations in F. Given a graph polynomial P (G; X) 2 F[X] and an element a 2 F, we view Pa(G) = P (G; a) as a graph parameter. We will look at the complexity of the problem of evaluating P (G; a) for a fixed a 2 F and at the problem of computing all the coefficients of P (G; X). Problem: P (G; a) Input: Graph G. Output: The value of P (G; X) for X = a. Problem: P (G; X) Input: Graph G. Output: All the coefficients of P (G; X) as a vector over F. We denote by TPa (n) the time needed to compute P (G; a) on graphs with n ver- tices in the Turing model of computation. Similarly TPX (n) denotes the time needed to compute all the coefficients of P (G; X). Clearly, for every a 2 F the problem P (G; a) is reducible to computing the coefficients of P (G; X). The converse is not true in gen- eral, but we shall see cases where for certain a0 2 F computing the coefficients of P (G; X) is reducible to P (G; a0). When we speak informally of the complexity spec- trum of P (G; X) we have in mind the variability of TPa (n) where a 2 F, without giving the term a precise definition. For a graph polynomial P (G; X), we are inter- ested in describing the complexity of Pa(G) for all a 2 F. A more modest task would be to describe it only for a 2 N. In the case of a 2 N we speak of the discrete com- plexity spectrum, in the case of a 2 F we speak of the full complexity spectrum, if the context requires it. 4If instead we use the binary cost model for computations in, say, Q, the main results still hold, but have to be formulated more carefully, as a 2 Q could be very large, and the notion of uniformity would be affected. 4 We define d EASY(P ) = fa 2 F : there exists d 2 N with TPa (n) ≤ n for all n ≥ 2g Analogously, we define #PHARD(P ) = fa 2 F : Pa(G) is #P-hardg: 1.2.
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